Sea target large error scatter point fusion method and system

Abstract

The application discloses a sea target large-error scattered point fusion method and system, relates to the technical field of data fusion, and solves the problems of fusion precision and non-smooth fusion track in the prior art.The fusion method comprises the following steps: after obtaining large-error scattered points and fusion configuration parameters, firstly, a flying point elimination module is used to classify the scattered points according to track numbers, and flying points are eliminated from each type of scattered points; then, for the remaining scattered points after the flying point elimination, an abnormal inflection point elimination module is used to eliminate abnormal inflection points in the scattered points; then, for the remaining scattered points after the inflection point elimination, a short-time window aggregation module is used for scattered point aggregation to generate aggregated points; finally, for the aggregated points, a sliding window smoothing module is used for track smoothing to generate smoothed points.

Description

Technical Field

[0001] This invention relates to the field of data fusion technology, and in particular to a method and system for fusing large-error scattered data of sea targets. Background Technology

[0002] The accuracy of scatter points for naval target positioning is affected by many factors, easily leading to significant positional deviations. These deviations are related to the location, altitude, and distribution of the positioning methods, as well as the accuracy of the positioning algorithm and timing statistics. This results in significant jitter in the scatter points, especially for slow-moving ship targets, where the true target position is often obscured by a large number of scatter points, making it difficult to determine the target's movement trend and trajectory. Therefore, it is necessary to fuse these numerous scatter points to generate a more accurate and smoother trajectory. For fusing scatter points from fast-moving targets or those with small errors, traditional methods primarily utilize the Kalman filter algorithm to suppress errors, generating relatively accurate trajectories. However, in practical engineering, due to larger errors and slower target movement, traditional filtering methods struggle to converge within a short timeframe. Furthermore, the positioning scatter point errors are no longer Gaussian distributed due to various factors, often containing flying points or anomalous inflection points. Traditional Kalman filters are ill-suited to fusing such noisy scatter points with unknown distributions. Therefore, there is an urgent need to research a smoothing method for scatter points with large errors and inherent anomalies, suitable for slow-moving targets with unknown distributions. Summary of the Invention

[0003] The purpose of this invention is to propose a method and system for fusion of large error scattered points of sea targets, and to solve the problems of low fusion accuracy and uneven track in the existing technology.

[0004] The technical solution adopted in this invention is as follows:

[0005] This invention is a method for fusion of large-error scattered points for sea targets, comprising the following steps:

[0006] Step S1: After obtaining the large error scatter points and fusion configuration parameters, first classify and save the scatter points according to the trajectory number. Scatter points with the same trajectory number are grouped into the same category. Scatter points in the same category are arranged in ascending order of time and saved into the same category. Then, for the first point, last point and middle point of the same category of scatter points, fly point judgment is performed. If it is judged as a fly point, the fly point is directly removed from the same category of scatter points.

[0007] Step S2: For the remaining scattered points after the flying point removal, the scattered points of the same type are segmented by inflection point according to the time of the scattered points. Scattered points with continuous time are grouped into the same segment, and scattered points with interrupted time are grouped into different segments. Then, abnormal inflection point judgment is performed on the midpoint of the scattered points in the same segment. If it is judged as an abnormal inflection point, the abnormal inflection point is directly removed from the scattered points in the same segment.

[0008] Step S3: For the remaining scattered points after removing abnormal inflection points, aggregate and segment scattered points of the same type according to the time of the scattered points, and group scattered points with similar times into the same segment; then for each segment, determine whether the number of scattered points in the segment is greater than 2. If so, remove extreme points in the position; finally, perform position aggregation and time aggregation on each segment of scattered points, and use the position aggregation result and time aggregation result as the aggregation point position and time to generate the aggregation point.

[0009] Step S4: For aggregation points, segment the same type of aggregation points according to the sliding window interruption time threshold and the sliding window interruption distance threshold. Then, extract sliding window data from the segment where the input aggregation point is located. Finally, for the sliding window data, determine whether the number of points in the sliding window data is less than 3. If it is, do not perform fitting and directly output the input aggregation point as a smooth point. Otherwise, use the first least squares algorithm to fit the x position, y position, and z position of the input aggregation point in the sliding window data respectively, and output the fitted point as a smooth point.

[0010] Furthermore, in step 1, the scattered points are first classified and saved according to the trajectory number. Scattered points with the same trajectory number are grouped into the same category. Scattered points in the same category are arranged in ascending order of time and saved into the same category. Then, the first point, last point and middle point of the scattered points in the same category are judged as flying points. If they are judged as flying points, they are directly removed from the scattered points in the same category.

[0011] Furthermore, in step 1, the flying point judgment is specifically divided into first point flying point judgment, last point flying point judgment, and intermediate point flying point judgment, specifically:

[0012] First-point flying point determination: Calculate the distance dis1 between the first point and the second point, and the distance dis2 between the first point and the third point. Take the minimum of dis1 and dis2 as the determination distance dis. Then, take the time difference disT between the second point and the first point as the movement time. Next, calculate the target's maximum movement distance disMax using the maximum speed, i.e., disMax = vMax * disT, where vMax is the maximum speed threshold for flying point elimination. Finally, determine whether dis > disMax - 2err. If it is true, the first point is a flying point; otherwise, the first point is not a flying point. Here, err is the positioning error of the first point.

[0013] For tail point flying point judgment, calculate the distance dis1 between the tail point and the second-to-last point, and the distance dis2 between the tail point and the third-to-last point. Take the minimum value of dis1 and dis2 as the judgment distance dis. Then, take the time difference disT between the tail point and the second-to-last point as the movement time. Then, use the maximum speed to calculate the maximum distance disMax of the target movement, i.e., disMax = vMax * disT, where vMax is the maximum speed threshold for flying point elimination. Finally, check whether dis > disMax - 2err. If it is true, it is a flying point; otherwise, it is not a flying point. Here, err is the positioning error of the tail point.

[0014] For intermediate point / flying point determination, calculate the distance dis1 between the input scatter point and the previous point, and the distance dis2 between the input scatter point and the next point. Take the minimum value of dis1 and dis2 as the determination distance dis. Then calculate the time difference disT1 between the input scatter point and the previous point, and the time difference disT2 between the input scatter point and the next point. Take the minimum value of disT1 and disT2 as the motion time disT. Then calculate the maximum distance disMax of the target motion using the maximum speed, i.e., disMax = vMax * disT, where vMax is the maximum speed threshold for flying point elimination. Finally, check if dis > disMax - 2err. If it is true, it is a flying point; otherwise, it is not a flying point. Here, err is the positioning error of the intermediate point.

[0015] Furthermore, in step 2, the method of segmenting the same type of scattered points according to the scattered point time is as follows: if the input scattered point is the first point of the scattered points of this type, then the first point is directly assigned to the first segment; otherwise, the time difference between the time of the input scattered point and the time of the point before the scattered point is calculated. If the time difference is greater than tMax1, then the input scattered point is assigned to a new segment; otherwise, the input scattered point is assigned to the segment to which the point before the scattered point belongs. Here, tMax1 is the interruption time threshold.

[0016] Furthermore, in step 2, the abnormal inflection point judgment method is as follows: First, calculate the distance dis1 between the input scatter point and the point before the input scatter point, calculate the distance dis2 between the input scatter point and the point after the input scatter point, and calculate the distance dis3 between the point before the input scatter point and the point after the input scatter point. Then, determine whether dis1>dis3+err is true. If it is true, the input scatter point is an abnormal inflection point. Otherwise, determine whether dis2>dis3+err is true. If it is true, the input scatter point is an abnormal inflection point. Otherwise, it is not. Here, err is the positioning error of the input scatter point.

[0017] Furthermore, in step 3, the aggregation segmentation method is as follows: if the input scatter point is the first point of this type of scatter point, then the first point is directly assigned to the first segment; otherwise, the time difference between the time of the input scatter point and the time difference between the first point of the segment to which the previous point belongs is calculated. If the time difference is greater than tMax2, then the input scatter point is assigned to a new segment; otherwise, the input scatter point is assigned to the segment to which the previous point belongs. Here, tMax2 is the aggregation time threshold.

[0018] Furthermore, in step 3, the location aggregation method is as follows: Let the number of scatter points in the scatter point sequence to be aggregated be n, and the x-coordinates in the x-coordinate scatter point sequence be x1, x2, ..., x... n The positioning errors in the x-coordinate scatter sequence are ex1, ex2, ..., ex n The y-coordinates in the scatter plot sequence are y1, y2, ..., y3. n The positioning errors in the y-coordinate scatter sequence are ey1, ey2, ..., ey1. n The z-coordinates in the scatter plot sequence are z1, z2, ..., z3. n The positioning errors in the z-coordinate scatter sequence are ez1, ez2, ..., ez n The elements after their aggregation are Then x, y, z are the coordinates of the aggregation point;

[0019] The time aggregation method is as follows: if there is only one scatter point in a segment, the aggregation time is the time of that scatter point; if there are only two scatter points in a segment, the aggregation time is the time of the larger of the two scatter points; if there are more than two scatter points in a segment, the arithmetic mean of the times of all scatter points in the segment is taken as the aggregation point time. Among them, t i Let be the time of the i-th scatter point in the segment, and n be the number of scatter points in the segment.

[0020] Furthermore, in step 4, the same type of aggregation points are segmented for interruption based on the sliding window interruption time threshold and the sliding window interruption distance threshold. Specifically:

[0021] If the input aggregation point is the first point of this type of aggregation point, then the first point is directly assigned to the first segment. Otherwise, calculate the time difference and Euclidean distance between the input aggregation point and the point before it. If the time difference is greater than tMax3 or the Euclidean distance is greater than dMax, then the input aggregation point is assigned to a new segment. Otherwise, the input aggregation point is assigned to the segment to which the point before it belongs.

[0022] Furthermore, in step 4, sliding window data is extracted from the segment where the input aggregation point is located. If the number of aggregation points preceding the input aggregation point in the segment is less than N-1, then the input aggregation point and all preceding aggregation points in the segment are used as sliding window data; otherwise, the input aggregation point and the N-1 preceding aggregation points in the segment are used as sliding window data.

[0023] This invention is a large-error scattered point fusion system for sea targets, comprising a flying point removal module, an abnormal inflection point removal module, an hourly window aggregation module, and a sliding window smoothing module that are electrically connected in sequence, wherein:

[0024] After obtaining the large error scattered points and fusion configuration parameters, the flying point elimination module classifies the scattered points according to the trajectory number and performs flying point elimination on each type of scattered points, outputting the scattered points to the abnormal inflection point elimination module.

[0025] The abnormal inflection point removal module is used to remove abnormal inflection points from the remaining scattered points after the flying point removal, and output the scattered points to the hourly window aggregation module.

[0026] The hourly window aggregation module aggregates the remaining scattered points after the inflection point is removed, generates an aggregated point, and outputs the aggregated point to the sliding window smoothing module.

[0027] The sliding window smoothing module is used to smooth the trajectory for aggregation points and generate smooth points.

[0028] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0029] This invention relates to a method and system for fusing large-error scattered points of sea targets. After obtaining large-error scattered points, the invention employs a flying point removal module to identify and remove flying points from the scattered points, eliminating their impact on fusion accuracy and improving trajectory accuracy. It also employs an inflection point removal module to identify and remove inflection points from the scattered points, eliminating their impact on fusion accuracy and improving trajectory accuracy. Furthermore, it employs an hourly window aggregation module to remove extreme points within the hourly window, reducing their interference with accuracy, and aggregating the trajectories within the hourly window into aggregated points, further improving trajectory accuracy. Finally, it employs a sliding window smoothing module to smooth the aggregated points within the sliding window using a least-squares algorithm, improving trajectory smoothness and exhibiting strong robustness against large-error trajectories. Attached Figure Description

[0030] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort, wherein:

[0031] Figure 1 This is a flowchart of the large error scatter point fusion processing of sea targets in this invention;

[0032] Figure 2 yes Figure 1 Flowchart of the mid-flying point rejection module;

[0033] Figure 3 yes Figure 1 Flowchart of the abnormal inflection point removal module;

[0034] Figure 4 yes Figure 1 Flowchart of the medium-sized window aggregation module;

[0035] Figure 5 yes Figure 1 Flowchart of the smoothing module for the middle sliding window. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only for explaining the invention and are not intended to limit the invention; that is, the described embodiments are merely some embodiments of the invention, and not all embodiments. The components of the embodiments of the invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0037] It should be noted that the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0038] Example 1

[0039] like Figures 1 to 5 As shown, in order to solve the problem of low fusion accuracy of large error scattered points, this invention provides a fusion method with high fusion accuracy, good trajectory smoothing effect, strong ability to suppress large errors, and insensitivity to flying points and abnormal inflection points, which can significantly improve fusion accuracy and trajectory smoothing effect.

[0040] See Figure 1To achieve the above objectives, this invention proposes a method for fusing large-error scattered points of sea targets, comprising the following steps: Step S1, after obtaining the large-error scattered points and fusion configuration parameters, a flying point removal module is used to classify the scattered points according to their trajectory numbers, and flying point removal is performed on each type of scattered point; Step S2, for the remaining scattered points after flying point removal, an abnormal inflection point removal module is used to remove abnormal inflection points from each type of scattered point; Step S3, for the remaining scattered points after inflection point removal, a small window aggregation module is used to aggregate the scattered points to generate aggregate points; Step S4, for the aggregate points, a sliding window smoothing module is used to smooth the trajectory to generate smooth points;

[0041] Among them, the large error scatter points mainly include trajectory number, time t, coordinate x, coordinate y, coordinate Z, and positioning error err;

[0042] The time is the difference between the current time and the system startup time, in seconds; the coordinates x, y, and Z are three-dimensional rectangular coordinates, in meters; the positioning error is the maximum distance error measured, in meters, which needs to be input according to business needs or given by the positioning system. In this embodiment, it is 10,000 meters.

[0043] The fusion configuration parameters mainly include the maximum speed threshold vMax for flying point rejection, the threshold tMax1 for inflection point interruption time, the threshold tMax2 for aggregation time, the threshold tMax3 for sliding window interruption time, the threshold dMax for sliding window interruption distance, and the threshold N for the number of sliding window points.

[0044] The unit of the maximum speed threshold for flying point elimination is meters per second, and in this embodiment it is 30 meters per second; the unit of the inflection point interruption time threshold is seconds, and in this embodiment it is 600 seconds; the units of the aggregation time threshold and the sliding window interruption time threshold are seconds, and in this embodiment they are 10 seconds and 1800 seconds, respectively; the unit of the sliding window interruption distance threshold is meters, and in this embodiment it is 60000 meters; the sliding window point number threshold is generally 5 to 60, and in this embodiment it is 10.

[0045] See Figure 2 In step S1, the process for removing flying points from the scattered points is as follows: First, the scattered points are classified and saved according to the trajectory number. Scattered points with the same trajectory number are grouped into the same category. Scattered points in the same category are arranged in ascending order of time and saved into the same category. Then, flying point judgment is performed on the first point, last point, and middle point of the scattered points in the same category. If it is judged to be a flying point, it is directly removed from the scattered points in the same category. Otherwise, the scattered points are output to the abnormal inflection point removal module.

[0046] The first point mentioned is the first point after arranging similar scattered points in ascending order of time;

[0047] The tail point mentioned above is the last point after arranging similar scattered points in ascending order of time;

[0048] The intermediate points mentioned above are the scattered points of the same type, excluding the first and last points, arranged in ascending order of time.

[0049] The method for determining the first point of failure is as follows: First, calculate the distance dis1 between the first point and the second point, and the distance dis2 between the first point and the third point. The distance calculation formula is the Euclidean distance formula. The minimum value of dis1 and dis2 is taken as the judgment distance dis, i.e., dis = min(dis1, dis2), where min means taking the minimum value of dis1 and dis2. Then, the time difference disT between the second point and the first point is taken as the movement time, i.e., disT = t2 - t1, where t2 is the time of the second point in the trajectory, and t1 is the time of the first point. Next, the maximum movement distance disMax of the target is calculated using the maximum speed. The calculation method is: maximum speed threshold for eliminating failure points * movement time, i.e., disMax = vMax * disT, where * means multiplication, and vMax is the maximum speed threshold for eliminating failure points. Finally, determine whether dis > disMax - 2err is true. If it is true, the first point is a failure point; otherwise, it is not. Here, err is the positioning error of the first point.

[0050] The tail point / flying point determination method is as follows: First, calculate the distance dis1 between the tail point and the second-to-last point, and the distance dis2 between the tail point and the third-to-last point. The distance calculation formula is the Euclidean distance formula. The minimum value of dis1 and dis2 is taken as the judgment distance dis, i.e., dis = min(dis1, dis2), where min means taking the minimum value of dis1 and dis2. Then, the time difference disT between the tail point and the second-to-last point is taken as the movement time, i.e., disT = t1 - t2, where t2 is the time of the second-to-last point and t1 is the time of the tail point. Next, the maximum distance disMax of the target movement is calculated using the maximum speed. The calculation method is: flying point rejection maximum speed threshold * movement time, i.e., disMax = vMax * disT, where * means multiplication, and vMax is the flying point rejection maximum speed threshold. Finally, determine whether dis > disMax - 2err is true. If it is true, it is a flying point; otherwise, it is not. Here, err is the positioning error of the tail point.

[0051] The method for determining intermediate point flying points is as follows: First, calculate the distance dis1 between the input scatter point and the previous point, and the distance dis2 between the input scatter point and the next point. The distance calculation formula is the Euclidean distance formula. The minimum value of dis1 and dis2 is taken as the judgment distance dis, i.e., dis = min(dis1, dis2), where min means taking the minimum value of dis1 and dis2. Then, calculate the time difference disT1 between the input scatter point and the previous point, and the time difference disT2 between the input scatter point and the next point. The minimum value of disT1 and disT2 is taken as the motion time disT, i.e., disT = min(disT1, disT2), where min means taking the minimum value of disT1 and disT2. Next, calculate the maximum distance disMax of the target motion using the maximum speed. The calculation method is: flying point elimination maximum speed threshold * motion time, i.e., disMax = vMax * disT, where * means multiplication, and vMax is the flying point elimination maximum speed threshold. Finally, determine whether dis > disMax - 2err is true. If it is true, it is a flying point; otherwise, it is not. Here, err is the positioning error of the intermediate point.

[0052] See Figure 3 In step S2, the process for removing abnormal inflection points from the scattered points is as follows: First, the scattered points of the same type are divided into inflection point segments according to the time of the scattered points. Scattered points with continuous time are grouped into the same segment, and scattered points with interrupted time are grouped into different segments. Then, abnormal inflection points are judged for the midpoint of the scattered points in the same segment. If it is judged to be an abnormal inflection point, the abnormal inflection point is directly removed from the scattered points in the same segment.

[0053] The interrupt segmentation method in the interrupt segmentation of the same type of scattered points according to the scattered point time is as follows: if the input scattered point is the first point of the scattered points of this type, then the first point is directly assigned to the first segment; otherwise, the time difference between the time of the input scattered point and the time of the point before the scattered point is calculated. If the time difference is greater than tMax1, then the input scattered point is assigned to a new segment; otherwise, the input scattered point is assigned to the segment to which the point before the scattered point belongs. Here, tMax1 is the interrupt time threshold.

[0054] The method for determining whether a point is a midpoint is as follows: Arrange all the scattered points in the segment containing the scattered point to be determined in ascending order of time. If the scattered point to be determined is not the first point after the arrangement, and is not the last point after the arrangement, then the scattered point to be determined is a midpoint; otherwise, it is not a midpoint.

[0055] The method for determining abnormal inflection points is as follows: First, calculate the distance dis1 between the input scatter point and the point before it, calculate the distance dis2 between the input scatter point and the point after it, and calculate the distance dis3 between the point before and the point after it. The distance calculation formula is the Euclidean distance formula. Then, determine whether dis1 > dis3 + err is true. If it is true, it is an abnormal inflection point. Otherwise, determine whether dis2 > dis3 + err is true. If it is true, it is an abnormal inflection point. Otherwise, it is not an abnormal inflection point. Here, err is the positioning error of the input scatter point.

[0056] See Figure 4 Step S3, the scatter point aggregation process is as follows: First, aggregate and segment scatter points of the same type according to their time, grouping scatter points with similar times into the same segment; then, for each segment, determine whether the number of scatter points in the segment is greater than 2. If so, remove extreme points by eliminating the extreme points in the position; finally, perform position aggregation and time aggregation on each segment of scatter points respectively, use the position aggregation result and time aggregation result as the aggregation point position and time to generate an aggregation point, and output the aggregation point to the sliding window smoothing module;

[0057] The aggregation and segmentation method is as follows: if the input scatter point is the first point of this type of scatter point, then the first point is directly assigned to the first segment; otherwise, the time difference between the time of the input scatter point and the time difference between the first point of the segment to which the previous point belongs is calculated. If the time difference is greater than tMax2, then the input scatter point is assigned to a new segment; otherwise, the input scatter point is assigned to the segment to which the previous point belongs. Here, tMax2 is the aggregation time threshold.

[0058] The extreme point removal method is as follows: sort the scattered points in the segment according to the x-coordinate, y-coordinate, and z-coordinate from smallest to largest, remove the first and last points after sorting, and save them to the x-coordinate scattered point sequence, y-coordinate scattered point sequence, and z-coordinate scattered point sequence to be aggregated.

[0059] The location aggregation method is as follows: Let the number of scatter points in the scatter point sequence to be aggregated be n, and the x-coordinates in the x-coordinate scatter point sequence be x1, x2, ..., x3. n The positioning errors in the x-coordinate scatter sequence are ex1, ex2, ..., ex n The y-coordinates in the scatter plot sequence are y1, y2, ..., y3. n The positioning errors in the y-coordinate scatter sequence are ey1, ey2, ..., ey1. n The z-coordinates in the scatter plot sequence are z1, z2, ..., z3. n The positioning errors in the z-coordinate scatter sequence are ez1, ez2, ..., ez n The elements after their aggregation are Then x, y, z are the coordinates of the aggregation point;

[0060] The time aggregation method is as follows: if there is only one scatter point in a segment, the aggregation time is the time of that scatter point; if there are only two scatter points in a segment, the aggregation time is the time of the larger of the two scatter points; if there are more than two scatter points in a segment, the arithmetic mean of all the scatter point times in the segment is taken as the aggregation point time. Among them, t i Let be the time of the i-th scatter point in the segment, and n be the number of scatter points in the segment.

[0061] See Figure 5 In step S4, the trajectory smoothing process is as follows: First, the same type of aggregation points are segmented according to the sliding window interruption time threshold and the sliding window interruption distance threshold. If the input aggregation point is the first point of this type of aggregation point, then the first point is directly assigned to the first segment; otherwise, the time difference and Euclidean distance between the input aggregation point and the point before it are calculated. If the time difference is greater than tMax3 or the Euclidean distance is greater than dMax, then the input aggregation point is assigned to a new segment; otherwise, the input aggregation point is assigned to the segment to which the point before it belongs. Then, the sliding window count is extracted from the segment where the input aggregation point is located. According to the algorithm, if the number of aggregate points preceding the input aggregate point in a segment is less than N-1, then the input aggregate point and all preceding aggregate points in the segment are used as sliding window data; otherwise, the input aggregate point and the preceding N-1 aggregate points in the segment are used as sliding window data. Finally, for the sliding window data, it is determined whether the number of points in the sliding window data is less than 3. If it is, no fitting is performed, and the input aggregate point is directly output as a smooth point; otherwise, the first least squares algorithm is used to fit the input aggregate point in the sliding window data at the x-position, y-position, and z-position respectively, and the fitted points are output as smooth points.

[0062] Wherein, tMax3 is the sliding window interruption time threshold, dMax is the sliding window interruption distance threshold, and N is the sliding window point number threshold;

[0063] The formula for fitting the x-position in the above-described method of using the first least squares algorithm to fit the x-position, y-position, and z-position of the input aggregation point in the sliding window data is as follows:

[0064]

[0065] The formula for fitting the Y position is:

[0066]

[0067] The Z-position fitting formula is:

[0068] in, m is the number of points in the sliding window data, t iLet x be the time of the i-th aggregation point in the sliding window data. i Let x be the x-coordinate of the i-th aggregation point in the sliding window data, and y be the y-coordinate of the i-th aggregation point. i Let z be the y-coordinate of the i-th aggregation point in the sliding window data. i Let t be the z-coordinate of the i-th aggregation point in the sliding window data. m Enter the aggregation point time.

[0069] Example 2

[0070] This invention is a large-error scattered point fusion system for sea targets, comprising a flying point elimination module, an abnormal inflection point elimination module, an hourly window aggregation module, and a sliding window smoothing module that are electrically connected in sequence.

[0071] in,

[0072] Flying point removal module: First, the scattered points are classified and saved according to the trajectory number. Scattered points with the same trajectory number are grouped into the same category. Scattered points in the same category are arranged in ascending order of time and saved into the same category. Then, flying point judgment is performed on the first point, last point and middle point of the scattered points in the same category. If it is judged to be a flying point, the flying point is directly removed from the scattered points in the same category and the scattered points are output to the abnormal inflection point removal module.

[0073] Abnormal Inflection Point Removal Module: First, the same type of scattered points are segmented by inflection point according to the scattered point time. Scattered points with continuous time are grouped into the same segment, and scattered points with interrupted time are grouped into different segments. Then, abnormal inflection point judgment is performed on the midpoint of the scattered points in the same segment. If it is judged to be an abnormal inflection point, the abnormal inflection point is directly removed from the scattered points in the same segment, and the scattered points are output to the hour window aggregation module.

[0074] The hourly window aggregation module first aggregates and segments scattered points of the same type according to their time, grouping scattered points with similar times into the same segment; then, for each segment, it checks whether the number of scattered points in the segment is greater than 2. If so, it removes extreme points by eliminating the extreme points in the position; finally, it performs position aggregation and time aggregation on each segment of scattered points, uses the position aggregation result and time aggregation result as the aggregation point position and time to generate an aggregation point, and outputs the aggregation point to the sliding window smoothing module;

[0075] The sliding window smoothing module first segments aggregate points of the same type based on the sliding window interruption time threshold and the sliding window interruption distance threshold. If the input aggregate point is the first point of that type of aggregate point, it is directly assigned to the first segment. Otherwise, the time difference and Euclidean distance between the input aggregate point and the point preceding it are calculated. If the time difference is greater than tMax3 or the Euclidean distance is greater than dMax, the input aggregate point is assigned to a new segment; otherwise, the input aggregate point is assigned to the segment to which the point preceding it belongs. Then, sliding window data is extracted from the segment where the input aggregate point is located. If the segment contains... If the number of aggregate points preceding the input aggregate point is less than N-1, then the input aggregate point and all preceding aggregate points in the segment are used as sliding window data; otherwise, the input aggregate point and the preceding N-1 aggregate points in the segment are used as sliding window data. Finally, for the sliding window data, it is determined whether the number of points in the sliding window data is less than 3. If it is, no fitting is performed, and the input aggregate point is directly output as a smooth point; otherwise, the first least squares algorithm is used to fit the input aggregate point in the sliding window data at the x-position, y-position, and z-position respectively, and the fitted points are output as smooth points.

[0076] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be conceived by those skilled in the art within the technical scope disclosed in the present invention without creative effort should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope defined in the claims.

Claims

1. A method for fusing scattered points with large errors in sea targets, characterized in that, Includes the following steps: Step S1: After obtaining the large error scatter points and fusion configuration parameters, the scatter points are classified according to their trajectory numbers, and flying point removal is performed on each type of scatter points. In Step 1, the scatter points are first classified and saved according to their trajectory numbers. Scatter points with the same trajectory number are grouped into the same category. Scatter points in the same category are arranged in ascending order of time and saved into the same category. Then, flying point judgment is performed on the first point, last point, and middle point of each type of scatter point. If it is judged to be a flying point, it is directly removed from the type of scatter points. In step 1, the flying point judgment is specifically divided into the first flying point judgment, the last flying point judgment, and the intermediate flying point judgment, specifically: First, determine the target's initial movement point. Calculate the distances dis1 between the first and second points, and dis2 between the first and third points. Use the minimum of dis1 and dis2 as the initial distance dis. Then, use the time difference disT between the second and first points as the movement time. Finally, calculate the target's maximum movement distance disMax using the maximum speed, i.e., disMax = vMax. disT and vMax are the maximum speed thresholds for excluding flying points. Finally, it is determined whether dis>disMax-2err holds true. If it does, the first point is a flying point; otherwise, the first point is not a flying point. Here, err is the positioning error of the first point. For tail point / fly point determination, calculate the distance dis1´ between the tail point and the second-to-last point, and the distance dis2´ between the tail point and the third-to-last point. Take the minimum of dis1´ and dis2´ as the determination distance dis´. Then, take the time difference disT´ between the tail point and the second-to-last point as the motion time. Finally, use the maximum speed to calculate the maximum distance disMax´ of the target's motion, i.e., disMax´ = vMax. disT´ and vMax are the maximum speed thresholds for eliminating flying points. Finally, it is determined whether dis´>disMax´-2err´ holds true. If it does, it is a flying point; otherwise, it is not a flying point. Here, err´ is the positioning error of the tail point. For midpoint fly-point judgment, calculate the distance dis1´´ between the input scatter point and the previous point, and the distance dis2´´ between the input scatter point and the next point. Take the minimum value of dis1´´ and dis2´´ as the judgment distance dis´´. Then calculate the time difference disT1´´ between the input scatter point and the previous point, and the time difference disT2´´ between the input scatter point and the next point. Take the minimum value of disT1´´ and disT2´´ as the motion time disT´´. Finally, use the maximum speed to calculate the maximum distance disMax´´ of the target's motion, i.e., disMax´´=vMax. disT´´, vMax is the maximum speed threshold for excluding flying points; finally, it is determined whether dis´´>disMax´´-2err´´ is true. If it is true, it is a flying point; otherwise, it is not a flying point. Here, err´´ is the positioning error of the intermediate point. Step S2: For the remaining scattered points after the flying point removal, the scattered points of the same type are segmented by inflection point according to the time of the scattered points. Scattered points with continuous time are grouped into the same segment, and scattered points with interrupted time are grouped into different segments. Then, abnormal inflection point judgment is performed on the midpoint of the scattered points in the same segment. If it is judged as an abnormal inflection point, the abnormal inflection point is directly removed from the scattered points in the same segment. Step S3: For the remaining scattered points after removing abnormal inflection points, aggregate and segment the scattered points of the same type according to the time of the scattered points; then for each segment, determine whether the number of scattered points in the segment is greater than 2. If so, remove the extreme points in the position; finally, perform position aggregation and time aggregation on each segment of scattered points, and use the position aggregation result and time aggregation result as the aggregation point position and time to generate the aggregation point. Step S4: For aggregation points, segment the same type of aggregation points according to the sliding window interruption time threshold and the sliding window interruption distance threshold. Then, extract sliding window data from the segment where the input aggregation point is located. Finally, for the sliding window data, determine whether the number of points in the sliding window data is less than 3. If it is, do not perform fitting and directly output the input aggregation point as a smooth point. Otherwise, use the first least squares algorithm to fit the x position, y position, and z position of the input aggregation point in the sliding window data respectively, and output the fitted point as a smooth point.

2. The method for fusion of large error scattered points of sea targets according to claim 1, characterized in that: In step 2, the method of segmenting the same type of scattered points by inflection point according to the scattered point time is as follows: if the input scattered point is the first point of the scattered points of this type, then the first point is directly assigned to the first segment; otherwise, the time difference between the time of the input scattered point and the time of the point before the scattered point is calculated. If the time difference is greater than tMax1, then the input scattered point is assigned to a new segment; otherwise, the input scattered point is assigned to the segment to which the point before the scattered point belongs. Here, tMax1 is the interruption time threshold.

3. The method for fusion of large error scattered points of sea targets according to claim 2, characterized in that, In step 2, the abnormal inflection point judgment method is as follows: First, calculate the distance dis1´´´ between the input scatter point and the point before the input scatter point, calculate the distance dis2´´´ between the input scatter point and the point after the input scatter point, and calculate the distance dis3´´´ between the point before the input scatter point and the point after the input scatter point. Then, determine whether dis1´´´>dis3´´´+err´´´ is true. If it is true, the input scatter point is an abnormal inflection point. Otherwise, determine whether dis2´´´>dis3´´´+err´´´ is true. If it is true, the input scatter point is an abnormal inflection point. Otherwise, it is not. Here, err´´´ is the positioning error of the input scatter point.

4. The method for fusion of large error scattered points of sea targets according to claim 1, characterized in that, In step 3, the aggregation segmentation method is as follows: if the input scatter point is the first point of this type of scatter point, then the first point is directly assigned to the first segment; otherwise, the time difference between the time of the input scatter point and the time difference between the first point of the segment to which the previous point belongs is calculated. If the time difference is greater than tMax2, then the input scatter point is assigned to a new segment; otherwise, the input scatter point is assigned to the segment to which the previous point belongs. Here, tMax2 is the aggregation time threshold.

5. The method for fusion of large error scattered points for sea targets according to claim 4, characterized in that, In step 3, the location aggregation method is as follows: Let the number of scatter points in the scatter point sequence to be aggregated be n, and the x-coordinates in the x-coordinate scatter point sequence be x1, x2, ..., x3. n The positioning errors in the x-coordinate scatter sequence are ex1, ex2, …, ex n The y-coordinates in the scatter plot sequence are y1, y2, ..., y3. n The positioning errors in the y-coordinate scatter sequence are ey1, ey2, …, ey1. n The z-coordinates in the scatter plot sequence are z1, z2, ..., z. n The positioning errors in the z-coordinate scatter sequence are ez1, ez2, …, ez n The elements after their aggregation are , , Then x, y, z are the coordinates of the aggregation point; The time aggregation method is as follows: if there is only one scatter point in a segment, the aggregation time is the time of that scatter point; if there are only two scatter points in a segment, the aggregation time is the longer of the two scatter points; if there are more than two scatter points in a segment, the arithmetic mean of all scatter point times in the segment is taken as the aggregation point time, i.e., aggregation point time t = , where t i Let be the time of the i-th scatter point in the segment.

6. The method for fusion of large error scattered points of sea targets according to claim 1, characterized in that, In step 4, the same type of aggregation points are segmented for interruption based on the sliding window interruption time threshold and the sliding window interruption distance threshold. Specifically: If the input aggregation point is the first point of this type of aggregation point, then the first point is directly assigned to the first segment. Otherwise, the time difference and Euclidean distance between the input aggregation point and the point before it are calculated. If the time difference is greater than the sliding window interruption time threshold tMax3 or the Euclidean distance is greater than the sliding window interruption distance threshold dMax, then the input aggregation point is assigned to a new segment. Otherwise, the input aggregation point is assigned to the segment to which the point before it belongs.

7. The method for fusion of large error scattered points for sea targets according to claim 6, characterized in that, In step 4, sliding window data is extracted from the segment where the input aggregation point is located. If the number of aggregation points before the input aggregation point in the segment is less than N-1, then the input aggregation point and all the aggregation points before it in the segment are used as sliding window data. Otherwise, the input aggregation point and the N-1 aggregation points before it in the segment are used as sliding window data. The threshold for the number of sliding window points is N.

8. A large-error scattered point fusion system for sea targets, characterized in that, This includes a flying point rejection module, an abnormal inflection point rejection module, an hourly window aggregation module, and a sliding window smoothing module, which are connected in sequence. The flying point removal module, after obtaining the large error scattered points and fusion configuration parameters, classifies the scattered points according to their trajectory numbers and removes flying points from each category, outputting the scattered points to the abnormal inflection point removal module. First, the scattered points are classified and saved according to their trajectory numbers, grouping scattered points with the same trajectory number into the same category. Scattered points within the same category are then sorted by time from smallest to largest and saved into the same category. Then, flying point judgment is performed on the first point, last point, and middle point of each category. If a flying point is determined, it is directly removed from the category. The flying point judgment specifically includes first point flying point judgment, last point flying point judgment, and middle point flying point judgment, as detailed below: First, determine the target's initial movement point. Calculate the distances dis1 between the first and second points, and dis2 between the first and third points. Use the minimum of dis1 and dis2 as the initial distance dis. Then, use the time difference disT between the second and first points as the movement time. Finally, calculate the target's maximum movement distance disMax using the maximum speed, i.e., disMax = vMax. disT and vMax are the maximum speed thresholds for excluding flying points. Finally, it is determined whether dis>disMax-2err holds true. If it does, the first point is a flying point; otherwise, the first point is not a flying point. Here, err is the positioning error of the first point. For tail point / fly point determination, calculate the distance dis1´ between the tail point and the second-to-last point, and the distance dis2´ between the tail point and the third-to-last point. Take the minimum of dis1´ and dis2´ as the determination distance dis´. Then, take the time difference disT´ between the tail point and the second-to-last point as the motion time. Finally, use the maximum speed to calculate the maximum distance disMax´ of the target's motion, i.e., disMax´ = vMax. disT´ and vMax are the maximum speed thresholds for eliminating flying points. Finally, it is determined whether dis´>disMax´-2err´ holds true. If it does, it is a flying point; otherwise, it is not a flying point. Here, err´ is the positioning error of the tail point. For midpoint fly-point judgment, calculate the distance dis1´´ between the input scatter point and the previous point, and the distance dis2´´ between the input scatter point and the next point. Take the minimum value of dis1´´ and dis2´´ as the judgment distance dis´´. Then calculate the time difference disT1´´ between the input scatter point and the previous point, and the time difference disT2´´ between the input scatter point and the next point. Take the minimum value of disT1´´ and disT2´´ as the motion time disT´´. Finally, use the maximum speed to calculate the maximum distance disMax´´ of the target's motion, i.e., disMax´´=vMax. disT´´, vMax is the maximum speed threshold for excluding flying points; finally, it is determined whether dis´´>disMax´´-2err´´ is true. If it is true, it is a flying point; otherwise, it is not a flying point. Here, err´´ is the positioning error of the intermediate point. The abnormal inflection point removal module first segments the same type of scattered points according to the scattered point time, grouping scattered points with continuous time into the same segment and scattered points with interrupted time into different segments; then, it judges abnormal inflection points for the midpoint of the scattered points in the same segment. If it is judged to be an abnormal inflection point, it directly removes the abnormal inflection point from the scattered points in the same segment and outputs the scattered points to the hour window aggregation module. The hourly window aggregation module first aggregates and segments scattered points of the same type according to their time, grouping scattered points with similar times into the same segment; then, for each segment, it checks whether the number of scattered points in the segment is greater than 2. If so, it removes extreme points by eliminating those in the position; finally, it performs position aggregation and time aggregation on each segment of scattered points, uses the position aggregation result and time aggregation result as the aggregation point position and time to generate an aggregation point, and outputs the aggregation point to the sliding window smoothing module; The sliding window smoothing module first segments the same type of aggregation points according to the sliding window interruption time threshold and the sliding window interruption distance threshold. Then, it extracts the sliding window data in the segment where the input aggregation point is located. Finally, for the sliding window data, it determines whether the number of points in the sliding window data is less than 3. If it is, no fitting is performed, and the input aggregation point is directly output as the smoothing point. Otherwise, the first least squares algorithm is used to fit the x-position, y-position, and z-position of the input aggregation point in the sliding window data, and the fitted point is output as the smoothing point.

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